code: def plot_interval_width_per_timestep(q_hats, method):
      """Plot the nonconformity scores as well as the one-sided interval width (q).
  
      Parameters
      ----------
          q_hats : dataframe
              prediction interval widths (or q) for each timestep, contains column ``q_hat_sym`` for symmetric q or
              ``q_hat_lo`` and ``q_hat_hi`` for asymmetric q
          method : str
              name of conformal prediction technique used
  
              Options
                  * (default) ``naive``: Naive or Absolute Residual
                  * ``cqr``: Conformalized Quantile Regression
  
      Returns
      -------
          matplotlib.pyplot.figure
              Figure showing the q-values for each timestep
      """
      fig, ax = plt.subplots()
      # check if q_hats contains q_hat_sym
      if "q_hat_sym" in q_hats.columns:
          q_hats_sym = q_hats["q_hat_sym"]
          ax.plot(range(1, len(q_hats_sym) + 1), q_hats_sym)
      else:
          q_hats_lo = q_hats["q_hat_lo"]
          q_hats_hi = q_hats["q_hat_hi"]
          ax.plot(
              range(1, len(q_hats_lo) + 1), q_hats_lo, label="lower q"
          )
          ax.plot(
              range(1, len(q_hats_hi) + 1), q_hats_hi, label="upper q"
          )
      ax.set_title(
          f"{method} One-Sided Interval Width with q per Timestep"
      )
      ax.set_xlabel("Timestep Number")
      ax.set_ylabel("One-Sided Interval Width")
      # ax.set_xlim(left=1)
      ax.set_ylim(bottom=0)
      return fig
  
file_path: neural_prophet/neuralprophet/plot_forecast_matplotlib.py
imports: import logging
  import numpy
  from neuralprophet.plot_model_parameters_matplotlib import plot_custom_season, plot_daily, plot_weekly, plot_yearly
  from neuralprophet.plot_utils import set_y_as_percent
label_desc: Use image node_id 6 for calling a global function with example usage: plot_interval_width_per_timestep(q_hats, method) and returns: fig